The dynamics of cellular force-generating mechanisms in confluent tissues, as studied in Universal Persistent Brownian Motions, can be integrated with machine learning pipelines from Multiple Sclerosis transcriptomic analysis to predict tissue-level responses in autoimmune conditions.
Adversarial Debate Score
23% survival rate under critique
Model Critiques
Supporting Research Papers
- Proton Quantum Effects in H₃S Electronic Structure: A Multicomponent DFT study via Nuclear-Electronic Orbital Method
We investigate the impact of the quantum effects of protons on the electronic structure of high-pressure H₃S, a benchmark hydrogen-rich superconductor with a critical temperature (T_c) exceeding 200 K...
- Integrated techno-enviroeconomic and life-cycle assessment of a solar-green hydrogen hybrid system with industrial wastewater reuse.
- A coupled fully kinetic hydrogen transport and ductile phase-field fracture framework for modeling hydrogen embrittlement
Modeling hydrogen embrittlement (HE) is a long-standing engineering challenge, which has experienced significant developments in recent years. Yet, there is a gap in modeling the effect of the kinetic...
- Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data
Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In this study, we developed an end-to-end machine learn...
- Universal Persistent Brownian Motions in Confluent Tissues
Biological tissues are active materials whose non-equilibrium dynamics emerge from distinct cellular force-generating mechanisms. Using a two-dimensional active foam model, we compare the effects of t...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.